BIOREMEDIATION KINETICS IN DIESEL OIL POLLUTED SOIL, AIMED TO GEOPHYSICAL MONITORING
Published In: 9TH INTERNATIONAL CONFERENCE ON ADVANCES IN BIO-INFORMATICS, BIO-TECHNOLOGY AND ENVIRONMENTAL ENGINEERING
Author(s): ALBERTO GODIO , BARBARA RUFFIN , FRANCESCA BOSCO , ANDREA VERGNANO , CARLA MARIA RAFFA , FULVIA CHIAMPO
Abstract: In this work the kinetics of aerobic bioremediation of diesel oil polluted soil was evaluated comparing microcosms at different values of water content (u%) and carbon to nitrogen ratio (C/N). The percentage of degraded diesel oil is influenced by these two parameters due to their relevance for microbial metabolism. In addition, water content influences substrate A. Soil II. Experimental dispersion and the contact between microorganisms and pollutant. The experimental runs allowed to model the process kinetics by the first and the second order model. In general, the best removal efficiency is achieved with C/N = 120 and u% = 8%, with the half-life time in the order of 70 days. On this base, a geophysical model was tested to predict the dielectrical permittivity of a sandy soil partially saturated with water, gas and diesel oil. The result of the modelling activi
- Publication Date: 08-Dec-2019
- DOI: 10.15224/978-1-63248-180-1-02
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ADVANCED DIAGNOSIS OF ALZHEIMER'S DISEASE BY AUTOMATICALLY OBTAINING THE BEST CORONAL SLICES FOR MULTI-CLASSIFICATION RECOGNITION
Published In: 9TH INTERNATIONAL CONFERENCE ON ADVANCES IN BIO-INFORMATICS, BIO-TECHNOLOGY AND ENVIRONMENTAL ENGINEERING
Author(s): ANTONIO CARRILLO , IGNACIO ROJAS , OLGA VALENZUELA
Abstract: The goal of this contribution is to find out a set of Y slices (coronal slices) from MRIs of patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal images, that provides the maximum accuracy in a multiclass classification system. Images are preprocessed and 2D wavelet coefficients are extracted to form a feature matrix. Using a feature selection algorithm called mRMR, the best features from the matrix are extracted; then, the dimension of the feature vectors is reduced using PCA and finally, it is used to train an SVM to perform multi-class classification. In order to find the best combinations of coronal slices, a multi-objective genetic optimization methodology based on NSGA-II is used and a set of different solutions are extracted from the Pareto front. More relevant solutions are selected using more flexible criteria than that of the Pareto front, and examine what slices and accuracies are achieved. The multi-classification accuracies obtained by the pr
- Publication Date: 08-Dec-2019
- DOI: 10.15224/978-1-63248-180-1-03
- Views: 0
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